• Korean Journal of Bronchoesophagology
• /
• v.1 no.1
• /
• pp.82-93
• /
• 1995

Three-dimensional reconstruction of computed tomographic image(3D CT) is a well-established imaging modality which has been investigated in various clinical settings. It is commonly performed in case of congenital or developmental abnormalities, and traumatic fracture of skull and face that requires reconstruction of osseous structure. However reporting the 3D CT in laryngeal or tracheal stenosis is rare and its results are obscure. The authors performed 3D CT in six cases of tracheal stenosis and found diagnostic value of 3D CT. A Comparision of diagnostic information obtained from plain X-ray, 2D CT and 3D CT has performed in total six cases of tracheal stenosis. Surgical treatment of the tracheal stenosis was following in these cases : tracheal end to end anastomosis In 1 case, laryngotracheal end to end anastomosis in 2 cases. 3D CT information was compared with operative finding. In two of six cases, satisfactory information was not obtained from 3D CT in evaluating an exact stenosis of trachea. Future, it will be helped in evaluating of tracheal stenosis by 3D CT.

• Journal of the Korean Society of Radiology
• /
• v.8 no.6
• /
• pp.357-364
• /
• 2014

The filtered back projection in the image reconstruction algorithms for the clinic computed tomography system has been widely used. Noise of the reconstructed image was examined under the input noise for parallel and fan beam geometries. The reconstruction images of $512{\times}512$ size were carried out under 360 and 720 projection by the Visual C++ for parallel beam and fan beam, respectively, and those agreed with the original Shepp-Logan head phantom very much. Noise was generated because of intrinsic restriction (finite number of projections) for the image reconstruction algorithm, filtered back projection, when no input noise was applied. Because the result noise was rapidly increased under 0.5% input noise ratio, technologies for reducing noise in CT system and image processing is important.

• Journal of the Korean Wood Science and Technology
• /
• v.33 no.5 s.133
• /
• pp.29-37
• /
• 2005

A previous study verified that the SIRT (simultaneous iterative reconstruction technique) method is more efficient than the back-projection method as a CT algorithm for wood. However, it was expected that the determination of the initial model function of the SIRT method would influence the quality of CT image. Therefore, in this study, we intended to develop a technique that could be used to determine an adequate initial model function. For this purpose, we proposed several techniques, and for each technique we examined the effects of the initial model function on the average errors and the CT image at each iteration. Through this study, it was shown that the average error was decreased and the image quality was improved using the proposed techniques. This tendency was most pronounced when the back-projection method was used to determine the initial model function. From the results of this study, we drew the following conclusions: 1) The initial model function of the SIRT method should be determined with careful attention, and 2) the back-projection method efficiently determines the initial model function of the SIRT method.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.22 no.1
• /
• pp.35-42
• /
• 2018

Purpose Standardized uptake value(SUV) has been widely used as a quantitative metric of uptake in PET/CT for diagnosis of malignant tumors and evaluation of tumor therapy response. However, the SUV depends on various factor including PET/CT scanner specifications and reconstruction parameter. The purpose of this study is to validate a EQ PET to evaluate SUV across different PET/CT systems. Materials and Methods First, NEMA IEC body phantom data were used to calculate the EQ filter for OSEM3D with PSF and TOF reconstruction from three different PET/CT systems in order to obtain EARL compliant recovery coefficients of each spheres. The Biograph true point 40 PET/CT images were reconstructed with a OSEM3D+PSF reconstruction, images of the Biograph mCT 40 and Biograph mCT 64 PET/CT scanners were reconstructed with a OSEM3D+PSF, OSEM3D+TOF, OSEM3D+PSF+TOF. Post reconstructions, the proprietary EQ filter was applied to the reconstruction data. Recovery coefficient can be estimated by ratio of measured to true activity concentration for spheres of different volume and coefficient variability(CV) value of RC for each sphere was compared. For clinical study, we compared SUVmax applying different reconstruction algorithms in FDG PET images of 61 patients with lung cancer using Biograph mCT 40 PET/CT scanner. Results For the phantom studied, the mean values of CV for OSEM3D, OSEM3D+PSF, OSEM3D+TOF and OSEM3D+PSF+TOF reconstructions were 0.05, 0.04, 0.04 and 0.03 respectively for RC. Application of the proprietary EQ filter, the mean values of CV for OSEM3D, OSEM3D+PSF, OSEM3D+TOF and OSEM3D+PSF+TOF reconstructions were 0.04, 0.03, 0.03 and 0.02 respectively for RC. Clinical study, there were no statistical significance of the difference applying EQ PET on SUVmax of 61 patients FDG PET image. (p=1.000) Conclusion This study indicates that CV values of RC in phantom were decreased after applying EQ PET for different PET/CT system and The EQ PET reduced reconstruction dependent variation in SUVs for 61 lung cancer patients, Therefore, EQ PET will be expected to provide accurate quantification when the patient is scanned on different PET/CT system.

• Korean Journal of Radiology
• /
• v.25 no.9
• /
• pp.833-842
• /
• 2024

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

• Journal of the Korean Society of Radiology
• /
• v.12 no.1
• /
• pp.85-91
• /
• 2018

Computed tomography, which obtains section images from reconstruction process using projection images, has been applied to various fields. The spatial resolution of the reconstructed image depends on the device used in CT system, the object, and the reconstruction process. In this paper, we investigates the effect of the number of projection images and the pixel size of the detector on the spatial resolution of the reconstructed image under the parallel beam geometry. The reconstruction program was written in Visual C++, and the matrix size of the reconstructed image was $512{\times}512$. The numerical bar phantom was constructed and the Min-Max method was introduced to evaluate the spatial resolution on the reconstructed image. When the number of projections used in reconstruction process was small, artifact like streak appeared and Min-Max was also low. The Min-Max showed upper saturation when the number of projections is increased. If the pixel size of the detector is reduced to 50% of the pixel size of the reconstructed image, the reconstructed image was perfectly recovered as the original phantom and the Min-Max decreased as increasing the detector pixel size. This study will be useful in determining the detector and the accuracy of rotation stage needed to achieve the spatial resolution required in the CT system.

• Journal of radiological science and technology
• /
• v.47 no.3
• /
• pp.175-182
• /
• 2024

Advanced modeled iterative reconstruction (ADMIRE) represents a repetitive reconstruction method that can adjust strength and kernel, each of which are known to affect computed tomography (CT) image quality. The aim of this study was to quantitatively analyze the noise and spatial resolution of CT images according to ADMIRE control factors. Patient images were obtained by applying ADMIRE strength 2 and 3, and kernel B40 and B59. For quantitative evaluations, the noise level, spatial resolution, and overall image quality were measured using coefficient of variation (COV), edge rise distance (ERD), and natural image quality evaluation (NIQE). The superior values for the average COV, ERD, and NIQE results were obtained for the ADMIRE reconstruction conditions of ADMIRE 2 + B40, ADMIRE 3 + B59, and ADMIRE3 + B59. NIQE, which represents the overall image quality based on no-reference, was about 6.04 when using ADMIRE 3 + B59, showing the best result among the reconstructed image acquisition conditions. The results of this study indicate that the ADMIRE strength and kernel chosen for use in ADMIRE reconstruction have a significant impact on CT image quality. This highlights the importance of adjusting to the control factors in consideration of the clinical environment.

• Journal of Biomedical Engineering Research
• /
• v.35 no.5
• /
• pp.132-141
• /
• 2014

In an ordinary CT scan, a large number of projections with full field-of-view (FFOV) are necessary to reconstruct high resolution images. However, excessive x-ray dosage is a great concern in FFOV scan. Region-of-interest (ROI) CT or sparse-view CT is considered to be a solution to reduce x-ray dosage in CT scanning, but it suffers from bright-band artifacts or streak artifacts giving contrast anomaly in the reconstructed image. In this study, we propose an image reconstruction method to eliminate the bright-band artifacts and the streak artifacts simultaneously. In addition to the ROI scan for the interior projection data with relatively high sampling rate in the view direction, we get sparse-view exterior projection data with much lower sampling rate. Then, we reconstruct images by solving a constrained total variation (TV) minimization problem for the interior projection data, which is assisted by the exterior projection data in the compressed sensing (CS) framework. For the interior image reconstruction assisted by the exterior projection data, we implemented the proposed method which enforces dual data fidelity terms and a TV term. The proposed method has effectively suppressed the bright-band artifacts around the ROI boundary and the streak artifacts in the ROI image. We expect the proposed method can be used for low-dose CT scans based on limited x-ray exposure to a small ROI in the human body.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.839-842
• /
• 2000

A new and modified neural network model Is proposed for CT image reconstruction from four projection directions only. The model uses the Resilient Back-Propagation (Rprop) algorithm, which is derived from the original Back-Propagation, for adaptation of its weights. In addition to the error in projection directions of the image being reconstructed, the proposed network makes use of errors in pixels between an image which passed the median filter and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.

• Journal of radiological science and technology
• /
• v.40 no.1
• /
• pp.79-85
• /
• 2017

The purpose of this study was to measure contrast to noise ratio (CNR) and signal to noise ratio (SNR) according to change of reconstruction from region of interest (ROI) in breast positron emission tomography-computed tomography (PET-CT), and to analyze the CNR and SNR statically. We examined images of breast PET-CT of 100 patients in a University-affiliated hospital, Seoul, Korea. Each patient's image of breast PET-CT were calculated by using ImageJ. Differences of CNR and SNR among four reconstruction algorithms were tested by SPSS Statistics21 ANOVA test for there was statistical significance (p<0.05). We have analysis socio-demographical variables, CNR and SNR according to reconstruction images, 95% confidence according to CNR and SNR of reconstruction and difference in a mean of CNR and SNR. SNR results, with the quality of distributions in the order of PSF_TOF, Iterative and Iterative-TOF, FBP-TOF. CNR, with the quality of distributions in the order of PSF_TOF, Iterative and Iterative-TOF, FBP-TOF. CNR and SNR of PET-CT reconstruction methods of the breast would be useful to evaluate breast diseases.